Overview

Dataset statistics

Number of variables16
Number of observations46646
Missing cells164464
Missing cells (%)22.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.0 MiB
Average record size in memory136.0 B

Variable types

Text12
Numeric4

Alerts

budget_in_usd is highly overall correlated with revenue_in_usdHigh correlation
revenue_in_usd is highly overall correlated with budget_in_usdHigh correlation
rating has 21675 (46.5%) missing valuesMissing
directors has 3338 (7.2%) missing valuesMissing
writers has 3338 (7.2%) missing valuesMissing
stars has 3338 (7.2%) missing valuesMissing
storyline has 4283 (9.2%) missing valuesMissing
origin_countries has 1985 (4.3%) missing valuesMissing
languages has 3211 (6.9%) missing valuesMissing
budget has 31898 (68.4%) missing valuesMissing
revenue has 29024 (62.2%) missing valuesMissing
revenue_in_usd has 29053 (62.3%) missing valuesMissing
budget_in_usd has 33174 (71.1%) missing valuesMissing
runtime_in_minutes is highly skewed (γ1 = 125.1698326)Skewed
id has unique valuesUnique
runtime_in_minutes has 11806 (25.3%) zerosZeros

Reproduction

Analysis started2024-07-06 00:38:35.939951
Analysis finished2024-07-06 00:38:40.430550
Duration4.49 seconds
Software versionydata-profiling v4.8.3
Download configurationconfig.json

Variables

id
Text

UNIQUE 

Distinct46646
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size728.8 KiB
2024-07-05T20:38:40.571177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length9
Mean length9.2462591
Min length9

Characters and Unicode

Total characters431301
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique46646 ?
Unique (%)100.0%

Sample

1st rowtt0060437
2nd rowtt0098300
3rd rowtt1853739
4th rowtt22036900
5th rowtt0419434
ValueCountFrequency (%)
tt0060437 1
 
< 0.1%
tt1260704 1
 
< 0.1%
tt0096869 1
 
< 0.1%
tt0071402 1
 
< 0.1%
tt1853739 1
 
< 0.1%
tt22036900 1
 
< 0.1%
tt0419434 1
 
< 0.1%
tt15073144 1
 
< 0.1%
tt0082406 1
 
< 0.1%
tt0421729 1
 
< 0.1%
Other values (46636) 46636
> 99.9%
2024-07-05T20:38:40.796189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
t 93292
21.6%
0 56078
13.0%
1 40769
9.5%
2 37590
8.7%
4 32543
 
7.5%
6 30754
 
7.1%
8 30442
 
7.1%
3 30120
 
7.0%
5 26841
 
6.2%
7 26837
 
6.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 431301
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 93292
21.6%
0 56078
13.0%
1 40769
9.5%
2 37590
8.7%
4 32543
 
7.5%
6 30754
 
7.1%
8 30442
 
7.1%
3 30120
 
7.0%
5 26841
 
6.2%
7 26837
 
6.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 431301
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 93292
21.6%
0 56078
13.0%
1 40769
9.5%
2 37590
8.7%
4 32543
 
7.5%
6 30754
 
7.1%
8 30442
 
7.1%
3 30120
 
7.0%
5 26841
 
6.2%
7 26837
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 431301
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 93292
21.6%
0 56078
13.0%
1 40769
9.5%
2 37590
8.7%
4 32543
 
7.5%
6 30754
 
7.1%
8 30442
 
7.1%
3 30120
 
7.0%
5 26841
 
6.2%
7 26837
 
6.2%

title
Text

Distinct43974
Distinct (%)94.4%
Missing73
Missing (%)0.2%
Memory size728.8 KiB
2024-07-05T20:38:40.964782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length146
Median length92
Mean length18.115818
Min length1

Characters and Unicode

Total characters843708
Distinct characters143
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique41944 ?
Unique (%)90.1%

Sample

1st rowFuneral in Berlin
2nd rowShag
3rd rowYou're Next
4th rowCovid Karma
5th rowAmerican Hardcore
ValueCountFrequency (%)
the 14763
 
9.9%
of 5228
 
3.5%
a 2048
 
1.4%
and 1695
 
1.1%
in 1436
 
1.0%
1386
 
0.9%
to 1049
 
0.7%
2 715
 
0.5%
movie 706
 
0.5%
story 663
 
0.4%
Other values (31442) 119361
80.1%
2024-07-05T20:38:41.202602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
102478
 
12.1%
e 81023
 
9.6%
a 58638
 
7.0%
o 50575
 
6.0%
n 46465
 
5.5%
i 45500
 
5.4%
r 43970
 
5.2%
t 40153
 
4.8%
s 31379
 
3.7%
h 30430
 
3.6%
Other values (133) 313097
37.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 843708
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
102478
 
12.1%
e 81023
 
9.6%
a 58638
 
7.0%
o 50575
 
6.0%
n 46465
 
5.5%
i 45500
 
5.4%
r 43970
 
5.2%
t 40153
 
4.8%
s 31379
 
3.7%
h 30430
 
3.6%
Other values (133) 313097
37.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 843708
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
102478
 
12.1%
e 81023
 
9.6%
a 58638
 
7.0%
o 50575
 
6.0%
n 46465
 
5.5%
i 45500
 
5.4%
r 43970
 
5.2%
t 40153
 
4.8%
s 31379
 
3.7%
h 30430
 
3.6%
Other values (133) 313097
37.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 843708
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
102478
 
12.1%
e 81023
 
9.6%
a 58638
 
7.0%
o 50575
 
6.0%
n 46465
 
5.5%
i 45500
 
5.4%
r 43970
 
5.2%
t 40153
 
4.8%
s 31379
 
3.7%
h 30430
 
3.6%
Other values (133) 313097
37.1%

rating
Real number (ℝ)

MISSING 

Distinct91
Distinct (%)0.4%
Missing21675
Missing (%)46.5%
Infinite0
Infinite (%)0.0%
Mean6.1354051
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size728.8 KiB
2024-07-05T20:38:41.287969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3.8
Q15.4
median6.3
Q37
95-th percentile7.9
Maximum10
Range9
Interquartile range (IQR)1.6

Descriptive statistics

Standard deviation1.2234213
Coefficient of variation (CV)0.19940351
Kurtosis0.6781104
Mean6.1354051
Median Absolute Deviation (MAD)0.8
Skewness-0.63457816
Sum153207.2
Variance1.4967596
MonotonicityNot monotonic
2024-07-05T20:38:41.345477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.4 953
 
2.0%
6.3 923
 
2.0%
6.6 916
 
2.0%
6.2 913
 
2.0%
6.1 894
 
1.9%
6.5 888
 
1.9%
6.8 874
 
1.9%
6.7 873
 
1.9%
7 773
 
1.7%
7.1 759
 
1.6%
Other values (81) 16205
34.7%
(Missing) 21675
46.5%
ValueCountFrequency (%)
1 3
 
< 0.1%
1.1 3
 
< 0.1%
1.2 4
 
< 0.1%
1.3 9
< 0.1%
1.4 7
< 0.1%
1.5 11
< 0.1%
1.6 8
< 0.1%
1.7 6
 
< 0.1%
1.8 14
< 0.1%
1.9 17
< 0.1%
ValueCountFrequency (%)
10 1
 
< 0.1%
9.9 1
 
< 0.1%
9.8 2
 
< 0.1%
9.7 3
 
< 0.1%
9.6 2
 
< 0.1%
9.5 3
 
< 0.1%
9.4 2
 
< 0.1%
9.3 8
< 0.1%
9.2 6
 
< 0.1%
9.1 19
< 0.1%

directors
Text

MISSING 

Distinct24480
Distinct (%)56.5%
Missing3338
Missing (%)7.2%
Memory size728.8 KiB
2024-07-05T20:38:41.519915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length106
Median length94
Mean length17.339175
Min length3

Characters and Unicode

Total characters750925
Distinct characters115
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique18118 ?
Unique (%)41.8%

Sample

1st row​Guy Hamilton
2nd row​Zelda Barron
3rd row​Adam Wingard
4th row​Biju Viswanath
5th row​Paul Rachman
ValueCountFrequency (%)
nonedirectors 1042
 
1.0%
​john 951
 
0.9%
​david 804
 
0.8%
​michael 781
 
0.7%
​robert 564
 
0.5%
​james 450
 
0.4%
​richard 429
 
0.4%
​peter 426
 
0.4%
​william 408
 
0.4%
​paul 382
 
0.4%
Other values (26303) 99478
94.1%
2024-07-05T20:38:41.780324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 63599
 
8.5%
62407
 
8.3%
e 57051
 
7.6%
​ 49549
 
6.6%
n 47167
 
6.3%
r 45543
 
6.1%
i 45511
 
6.1%
o 41064
 
5.5%
l 28986
 
3.9%
s 24629
 
3.3%
Other values (105) 285419
38.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 750925
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 63599
 
8.5%
62407
 
8.3%
e 57051
 
7.6%
​ 49549
 
6.6%
n 47167
 
6.3%
r 45543
 
6.1%
i 45511
 
6.1%
o 41064
 
5.5%
l 28986
 
3.9%
s 24629
 
3.3%
Other values (105) 285419
38.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 750925
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 63599
 
8.5%
62407
 
8.3%
e 57051
 
7.6%
​ 49549
 
6.6%
n 47167
 
6.3%
r 45543
 
6.1%
i 45511
 
6.1%
o 41064
 
5.5%
l 28986
 
3.9%
s 24629
 
3.3%
Other values (105) 285419
38.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 750925
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 63599
 
8.5%
62407
 
8.3%
e 57051
 
7.6%
​ 49549
 
6.6%
n 47167
 
6.3%
r 45543
 
6.1%
i 45511
 
6.1%
o 41064
 
5.5%
l 28986
 
3.9%
s 24629
 
3.3%
Other values (105) 285419
38.0%

writers
Text

MISSING 

Distinct38540
Distinct (%)89.0%
Missing3338
Missing (%)7.2%
Memory size728.8 KiB
2024-07-05T20:38:41.983168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length153
Median length83
Mean length33.318186
Min length3

Characters and Unicode

Total characters1442944
Distinct characters122
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique35839 ?
Unique (%)82.8%

Sample

1st row​Len Deighton, ​Evan Jones
2nd row, ​Lanier Laney, ​Terry Sweeney, ​Robin Swicord,
3rd row​Simon Barrett
4th rownoneStars, ​Rodney Norman, ​Christine Ohlman, ​Christopher Annino, noneStars
5th rownoneWriter, ​Steven Blush, noneWriter
ValueCountFrequency (%)
9528
 
4.8%
nonestars 4508
 
2.3%
nonewriter 2154
 
1.1%
​john 1541
 
0.8%
​david 1401
 
0.7%
​michael 1177
 
0.6%
​robert 1045
 
0.5%
​james 771
 
0.4%
​william 674
 
0.3%
​paul 659
 
0.3%
Other values (43274) 173883
88.1%
2024-07-05T20:38:42.292084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
163559
 
11.3%
a 115170
 
8.0%
e 104154
 
7.2%
n 90117
 
6.2%
​ 85631
 
5.9%
r 84316
 
5.8%
i 77553
 
5.4%
o 72915
 
5.1%
, 68037
 
4.7%
l 50629
 
3.5%
Other values (112) 530863
36.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1442944
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
163559
 
11.3%
a 115170
 
8.0%
e 104154
 
7.2%
n 90117
 
6.2%
​ 85631
 
5.9%
r 84316
 
5.8%
i 77553
 
5.4%
o 72915
 
5.1%
, 68037
 
4.7%
l 50629
 
3.5%
Other values (112) 530863
36.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1442944
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
163559
 
11.3%
a 115170
 
8.0%
e 104154
 
7.2%
n 90117
 
6.2%
​ 85631
 
5.9%
r 84316
 
5.8%
i 77553
 
5.4%
o 72915
 
5.1%
, 68037
 
4.7%
l 50629
 
3.5%
Other values (112) 530863
36.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1442944
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
163559
 
11.3%
a 115170
 
8.0%
e 104154
 
7.2%
n 90117
 
6.2%
​ 85631
 
5.9%
r 84316
 
5.8%
i 77553
 
5.4%
o 72915
 
5.1%
, 68037
 
4.7%
l 50629
 
3.5%
Other values (112) 530863
36.8%

stars
Text

MISSING 

Distinct42000
Distinct (%)97.0%
Missing3338
Missing (%)7.2%
Memory size728.8 KiB
2024-07-05T20:38:42.484874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length106
Median length90
Mean length45.772121
Min length3

Characters and Unicode

Total characters1982299
Distinct characters123
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique41187 ?
Unique (%)95.1%

Sample

1st row, ​Michael Caine, ​Oscar Homolka, ​Paul Hubschmid,
2nd row, ​Phoebe Cates, ​Bridget Fonda, ​Scott Coffey,
3rd row, ​Sharni Vinson, ​Joe Swanberg, ​AJ Bowen,
4th row​Biju Viswanath
5th row, ​Greg Ginn, ​Ian MacKaye, ​James Drescher,
ValueCountFrequency (%)
35961
 
12.8%
​john 1623
 
0.6%
​michael 1284
 
0.5%
​robert 1113
 
0.4%
​david 997
 
0.4%
​james 938
 
0.3%
​richard 816
 
0.3%
​peter 666
 
0.2%
​tom 644
 
0.2%
de 638
 
0.2%
Other values (49683) 235631
84.1%
2024-07-05T20:38:42.738604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
272958
13.8%
a 157650
 
8.0%
, 147410
 
7.4%
e 133312
 
6.7%
​ 118386
 
6.0%
n 110170
 
5.6%
i 102779
 
5.2%
r 99811
 
5.0%
o 87730
 
4.4%
l 71270
 
3.6%
Other values (113) 680823
34.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1982299
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
272958
13.8%
a 157650
 
8.0%
, 147410
 
7.4%
e 133312
 
6.7%
​ 118386
 
6.0%
n 110170
 
5.6%
i 102779
 
5.2%
r 99811
 
5.0%
o 87730
 
4.4%
l 71270
 
3.6%
Other values (113) 680823
34.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1982299
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
272958
13.8%
a 157650
 
8.0%
, 147410
 
7.4%
e 133312
 
6.7%
​ 118386
 
6.0%
n 110170
 
5.6%
i 102779
 
5.2%
r 99811
 
5.0%
o 87730
 
4.4%
l 71270
 
3.6%
Other values (113) 680823
34.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1982299
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
272958
13.8%
a 157650
 
8.0%
, 147410
 
7.4%
e 133312
 
6.7%
​ 118386
 
6.0%
n 110170
 
5.6%
i 102779
 
5.2%
r 99811
 
5.0%
o 87730
 
4.4%
l 71270
 
3.6%
Other values (113) 680823
34.3%

storyline
Text

MISSING 

Distinct42231
Distinct (%)99.7%
Missing4283
Missing (%)9.2%
Memory size728.8 KiB
2024-07-05T20:38:42.946898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length382
Median length301
Mean length184.66867
Min length3

Characters and Unicode

Total characters7823119
Distinct characters153
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique42191 ?
Unique (%)99.6%

Sample

1st rowSent to East Berlin to retrieve a Communist defector, British spy Harry Palmer suspects the situation is not what his superiors believe it to be.
2nd rowSummer of 1963. Carson is getting married to her boyfriend so her friends Melaina, Pudge and Luanne take her to Myrtle Beach for one last irresponsible weekend.
3rd rowWhen the Davison family comes under attack during their wedding anniversary getaway, the gang of mysterious killers soon learns that one of the victims harbors a secret talent for fighting back.
4th rowAn Indian film maker is stuck in USA for five months because of global pandemic. During this period, he repeatedly tries to make films, to maintain his sanity. The filmmaking process during Covid was a journey to hell; which teaches the filmmaker about his own disabilities and the screwed-up ways karma works.
5th rowThe History of American Punk Rock 1980-1986
ValueCountFrequency (%)
the 76519
 
5.7%
a 63258
 
4.8%
of 43400
 
3.3%
and 39761
 
3.0%
to 38670
 
2.9%
in 27138
 
2.0%
his 19214
 
1.4%
is 15728
 
1.2%
with 11565
 
0.9%
an 11363
 
0.9%
Other values (65230) 984868
74.0%
2024-07-05T20:38:43.224414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1289087
16.5%
e 735285
 
9.4%
a 527757
 
6.7%
t 504295
 
6.4%
i 463495
 
5.9%
o 456506
 
5.8%
n 452106
 
5.8%
r 421180
 
5.4%
s 406370
 
5.2%
h 307209
 
3.9%
Other values (143) 2259829
28.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7823119
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1289087
16.5%
e 735285
 
9.4%
a 527757
 
6.7%
t 504295
 
6.4%
i 463495
 
5.9%
o 456506
 
5.8%
n 452106
 
5.8%
r 421180
 
5.4%
s 406370
 
5.2%
h 307209
 
3.9%
Other values (143) 2259829
28.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7823119
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1289087
16.5%
e 735285
 
9.4%
a 527757
 
6.7%
t 504295
 
6.4%
i 463495
 
5.9%
o 456506
 
5.8%
n 452106
 
5.8%
r 421180
 
5.4%
s 406370
 
5.2%
h 307209
 
3.9%
Other values (143) 2259829
28.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7823119
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1289087
16.5%
e 735285
 
9.4%
a 527757
 
6.7%
t 504295
 
6.4%
i 463495
 
5.9%
o 456506
 
5.8%
n 452106
 
5.8%
r 421180
 
5.4%
s 406370
 
5.2%
h 307209
 
3.9%
Other values (143) 2259829
28.9%

origin_countries
Text

MISSING 

Distinct3911
Distinct (%)8.8%
Missing1985
Missing (%)4.3%
Memory size728.8 KiB
2024-07-05T20:38:43.381838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length176
Median length175
Mean length14.212736
Min length4

Characters and Unicode

Total characters634755
Distinct characters57
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3037 ?
Unique (%)6.8%

Sample

1st rowUnited Kingdom
2nd rowUnited States, United Kingdom
3rd rowUnited States, United Kingdom
4th rowUnited States
5th rowUnited States
ValueCountFrequency (%)
united 28865
30.9%
states 23630
25.3%
kingdom 5140
 
5.5%
france 3035
 
3.2%
japan 2759
 
3.0%
india 2660
 
2.8%
germany 2431
 
2.6%
canada 2381
 
2.5%
italy 2063
 
2.2%
spain 1308
 
1.4%
Other values (219) 19128
20.5%
2024-07-05T20:38:43.605720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
t 83147
13.1%
e 68251
10.8%
a 62673
9.9%
n 58359
9.2%
48739
 
7.7%
i 48655
 
7.7%
d 42272
 
6.7%
U 29242
 
4.6%
s 28662
 
4.5%
S 27297
 
4.3%
Other values (47) 137458
21.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 634755
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 83147
13.1%
e 68251
10.8%
a 62673
9.9%
n 58359
9.2%
48739
 
7.7%
i 48655
 
7.7%
d 42272
 
6.7%
U 29242
 
4.6%
s 28662
 
4.5%
S 27297
 
4.3%
Other values (47) 137458
21.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 634755
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 83147
13.1%
e 68251
10.8%
a 62673
9.9%
n 58359
9.2%
48739
 
7.7%
i 48655
 
7.7%
d 42272
 
6.7%
U 29242
 
4.6%
s 28662
 
4.5%
S 27297
 
4.3%
Other values (47) 137458
21.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 634755
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 83147
13.1%
e 68251
10.8%
a 62673
9.9%
n 58359
9.2%
48739
 
7.7%
i 48655
 
7.7%
d 42272
 
6.7%
U 29242
 
4.6%
s 28662
 
4.5%
S 27297
 
4.3%
Other values (47) 137458
21.7%

languages
Text

MISSING 

Distinct3338
Distinct (%)7.7%
Missing3211
Missing (%)6.9%
Memory size728.8 KiB
2024-07-05T20:38:43.789028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length165
Median length7
Mean length10.486865
Min length4

Characters and Unicode

Total characters455497
Distinct characters62
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2555 ?
Unique (%)5.9%

Sample

1st rowEnglish, German
2nd rowEnglish
3rd rowEnglish
4th rowEnglish
5th rowEnglish
ValueCountFrequency (%)
english 30666
50.3%
french 3321
 
5.4%
spanish 3299
 
5.4%
japanese 2796
 
4.6%
german 2460
 
4.0%
italian 2086
 
3.4%
russian 1546
 
2.5%
hindi 1429
 
2.3%
mandarin 993
 
1.6%
korean 711
 
1.2%
Other values (247) 11633
 
19.1%
2024-07-05T20:38:44.045197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n 57867
12.7%
i 49842
10.9%
s 44134
9.7%
h 40494
8.9%
l 36001
 
7.9%
g 33381
 
7.3%
E 30728
 
6.7%
a 29529
 
6.5%
e 19530
 
4.3%
17505
 
3.8%
Other values (52) 96486
21.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 455497
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 57867
12.7%
i 49842
10.9%
s 44134
9.7%
h 40494
8.9%
l 36001
 
7.9%
g 33381
 
7.3%
E 30728
 
6.7%
a 29529
 
6.5%
e 19530
 
4.3%
17505
 
3.8%
Other values (52) 96486
21.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 455497
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 57867
12.7%
i 49842
10.9%
s 44134
9.7%
h 40494
8.9%
l 36001
 
7.9%
g 33381
 
7.3%
E 30728
 
6.7%
a 29529
 
6.5%
e 19530
 
4.3%
17505
 
3.8%
Other values (52) 96486
21.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 455497
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 57867
12.7%
i 49842
10.9%
s 44134
9.7%
h 40494
8.9%
l 36001
 
7.9%
g 33381
 
7.3%
E 30728
 
6.7%
a 29529
 
6.5%
e 19530
 
4.3%
17505
 
3.8%
Other values (52) 96486
21.2%

budget
Text

MISSING 

Distinct2891
Distinct (%)19.6%
Missing31898
Missing (%)68.4%
Memory size728.8 KiB
2024-07-05T20:38:44.209644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length31
Median length30
Mean length22.079197
Min length14

Characters and Unicode

Total characters325624
Distinct characters55
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2003 ?
Unique (%)13.6%

Sample

1st row$5,000,000 (estimated)
2nd row$1,000,000 (estimated)
3rd row$12,000,000 (estimated)
4th row$40,000,000 (estimated)
5th row₹1,000,000 (estimated)
ValueCountFrequency (%)
estimated 14748
48.9%
5,000,000 358
 
1.2%
20,000,000 349
 
1.2%
10,000,000 339
 
1.1%
1,000,000 335
 
1.1%
15,000,000 303
 
1.0%
2,000,000 302
 
1.0%
25,000,000 276
 
0.9%
30,000,000 272
 
0.9%
3,000,000 269
 
0.9%
Other values (2538) 12604
41.8%
2024-07-05T20:38:44.430244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 79575
24.4%
e 29496
 
9.1%
t 29496
 
9.1%
, 25864
 
7.9%
m 14748
 
4.5%
d 14748
 
4.5%
14748
 
4.5%
( 14748
 
4.5%
s 14748
 
4.5%
i 14748
 
4.5%
Other values (45) 72705
22.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 325624
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 79575
24.4%
e 29496
 
9.1%
t 29496
 
9.1%
, 25864
 
7.9%
m 14748
 
4.5%
d 14748
 
4.5%
14748
 
4.5%
( 14748
 
4.5%
s 14748
 
4.5%
i 14748
 
4.5%
Other values (45) 72705
22.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 325624
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 79575
24.4%
e 29496
 
9.1%
t 29496
 
9.1%
, 25864
 
7.9%
m 14748
 
4.5%
d 14748
 
4.5%
14748
 
4.5%
( 14748
 
4.5%
s 14748
 
4.5%
i 14748
 
4.5%
Other values (45) 72705
22.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 325624
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 79575
24.4%
e 29496
 
9.1%
t 29496
 
9.1%
, 25864
 
7.9%
m 14748
 
4.5%
d 14748
 
4.5%
14748
 
4.5%
( 14748
 
4.5%
s 14748
 
4.5%
i 14748
 
4.5%
Other values (45) 72705
22.3%

revenue
Text

MISSING 

Distinct17459
Distinct (%)99.1%
Missing29024
Missing (%)62.2%
Memory size728.8 KiB
2024-07-05T20:38:44.626017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length13
Mean length9.3874135
Min length2

Characters and Unicode

Total characters165425
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique17307 ?
Unique (%)98.2%

Sample

1st row$183
2nd row$6,957,975
3rd row$26,895,481
4th row$376,057
5th row$63,456,988
ValueCountFrequency (%)
46,808 6
 
< 0.1%
824 3
 
< 0.1%
2,300,000 3
 
< 0.1%
3,500,000 3
 
< 0.1%
2,684 3
 
< 0.1%
160 3
 
< 0.1%
14,000,000 3
 
< 0.1%
10,000,000 3
 
< 0.1%
3,414 3
 
< 0.1%
4,223 3
 
< 0.1%
Other values (17434) 17590
99.8%
2024-07-05T20:38:44.873306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
, 27870
16.8%
$ 17591
10.6%
1 15784
9.5%
2 13227
8.0%
3 12184
7.4%
0 11788
7.1%
4 11660
7.0%
5 11174
6.8%
6 11140
 
6.7%
7 10889
 
6.6%
Other values (8) 22118
13.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 165425
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
, 27870
16.8%
$ 17591
10.6%
1 15784
9.5%
2 13227
8.0%
3 12184
7.4%
0 11788
7.1%
4 11660
7.0%
5 11174
6.8%
6 11140
 
6.7%
7 10889
 
6.6%
Other values (8) 22118
13.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 165425
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
, 27870
16.8%
$ 17591
10.6%
1 15784
9.5%
2 13227
8.0%
3 12184
7.4%
0 11788
7.1%
4 11660
7.0%
5 11174
6.8%
6 11140
 
6.7%
7 10889
 
6.6%
Other values (8) 22118
13.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 165425
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
, 27870
16.8%
$ 17591
10.6%
1 15784
9.5%
2 13227
8.0%
3 12184
7.4%
0 11788
7.1%
4 11660
7.0%
5 11174
6.8%
6 11140
 
6.7%
7 10889
 
6.6%
Other values (8) 22118
13.4%
Distinct299
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size728.8 KiB
2024-07-05T20:38:44.991434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length17
Mean length14.373473
Min length0

Characters and Unicode

Total characters670465
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique77 ?
Unique (%)0.2%

Sample

1st row1 hour 42 minutes
2nd row1 hour 38 minutes
3rd row1 hour 35 minutes
4th row1 hour 1 minute
5th row1 hour 40 minutes
ValueCountFrequency (%)
minutes 39333
25.0%
1 32787
20.9%
hour 32407
20.6%
hours 6390
 
4.1%
2 6367
 
4.1%
30 2138
 
1.4%
35 1189
 
0.8%
40 1187
 
0.8%
25 1086
 
0.7%
20 1042
 
0.7%
Other values (57) 33094
21.1%
2024-07-05T20:38:45.272387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
116235
17.3%
u 78510
11.7%
s 45723
 
6.8%
1 40590
 
6.1%
m 39713
 
5.9%
e 39713
 
5.9%
t 39713
 
5.9%
i 39713
 
5.9%
n 39713
 
5.9%
r 38797
 
5.8%
Other values (11) 152045
22.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 670465
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
116235
17.3%
u 78510
11.7%
s 45723
 
6.8%
1 40590
 
6.1%
m 39713
 
5.9%
e 39713
 
5.9%
t 39713
 
5.9%
i 39713
 
5.9%
n 39713
 
5.9%
r 38797
 
5.8%
Other values (11) 152045
22.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 670465
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
116235
17.3%
u 78510
11.7%
s 45723
 
6.8%
1 40590
 
6.1%
m 39713
 
5.9%
e 39713
 
5.9%
t 39713
 
5.9%
i 39713
 
5.9%
n 39713
 
5.9%
r 38797
 
5.8%
Other values (11) 152045
22.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 670465
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
116235
17.3%
u 78510
11.7%
s 45723
 
6.8%
1 40590
 
6.1%
m 39713
 
5.9%
e 39713
 
5.9%
t 39713
 
5.9%
i 39713
 
5.9%
n 39713
 
5.9%
r 38797
 
5.8%
Other values (11) 152045
22.7%

genres
Text

Distinct1063
Distinct (%)2.3%
Missing74
Missing (%)0.2%
Memory size728.8 KiB
2024-07-05T20:38:45.365748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length34
Median length28
Mean length19.512583
Min length3

Characters and Unicode

Total characters908740
Distinct characters36
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique303 ?
Unique (%)0.7%

Sample

1st rowThriller
2nd rowComedy, Drama, Romance
3rd rowDrama, Horror, Thriller
4th rowBiography, Comedy
5th rowDocumentary, History, Music
ValueCountFrequency (%)
drama 17302
15.5%
action 11601
10.4%
biography 10026
9.0%
animation 9693
 
8.7%
comedy 8360
 
7.5%
documentary 7317
 
6.6%
adventure 6785
 
6.1%
thriller 5831
 
5.2%
crime 5587
 
5.0%
horror 4457
 
4.0%
Other values (15) 24327
21.9%
2024-07-05T20:38:45.521909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
r 83171
 
9.2%
a 75547
 
8.3%
i 65473
 
7.2%
, 64714
 
7.1%
64714
 
7.1%
o 63159
 
7.0%
m 54061
 
5.9%
n 53539
 
5.9%
e 51360
 
5.7%
t 47255
 
5.2%
Other values (26) 285747
31.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 908740
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 83171
 
9.2%
a 75547
 
8.3%
i 65473
 
7.2%
, 64714
 
7.1%
64714
 
7.1%
o 63159
 
7.0%
m 54061
 
5.9%
n 53539
 
5.9%
e 51360
 
5.7%
t 47255
 
5.2%
Other values (26) 285747
31.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 908740
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 83171
 
9.2%
a 75547
 
8.3%
i 65473
 
7.2%
, 64714
 
7.1%
64714
 
7.1%
o 63159
 
7.0%
m 54061
 
5.9%
n 53539
 
5.9%
e 51360
 
5.7%
t 47255
 
5.2%
Other values (26) 285747
31.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 908740
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 83171
 
9.2%
a 75547
 
8.3%
i 65473
 
7.2%
, 64714
 
7.1%
64714
 
7.1%
o 63159
 
7.0%
m 54061
 
5.9%
n 53539
 
5.9%
e 51360
 
5.7%
t 47255
 
5.2%
Other values (26) 285747
31.4%

revenue_in_usd
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct17416
Distinct (%)99.0%
Missing29053
Missing (%)62.3%
Infinite0
Infinite (%)0.0%
Mean39741230
Minimum3
Maximum2.923706 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size728.8 KiB
2024-07-05T20:38:45.596684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile5899.6
Q1174036
median2443487
Q322227514
95-th percentile2.021434 × 108
Maximum2.923706 × 109
Range2.923706 × 109
Interquartile range (IQR)22053478

Descriptive statistics

Standard deviation1.2317535 × 108
Coefficient of variation (CV)3.0994348
Kurtosis89.358949
Mean39741230
Median Absolute Deviation (MAD)2432700
Skewness7.5211205
Sum6.9916746 × 1011
Variance1.5172167 × 1016
MonotonicityNot monotonic
2024-07-05T20:38:45.656037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
46808 6
 
< 0.1%
14000000 3
 
< 0.1%
3414 3
 
< 0.1%
2300000 3
 
< 0.1%
160 3
 
< 0.1%
2684 3
 
< 0.1%
10000000 3
 
< 0.1%
3000000 3
 
< 0.1%
4223 3
 
< 0.1%
11000000 3
 
< 0.1%
Other values (17406) 17560
37.6%
(Missing) 29053
62.3%
ValueCountFrequency (%)
3 1
< 0.1%
4 1
< 0.1%
6 1
< 0.1%
9 1
< 0.1%
13 1
< 0.1%
19 1
< 0.1%
24 2
< 0.1%
26 1
< 0.1%
30 1
< 0.1%
31 1
< 0.1%
ValueCountFrequency (%)
2923706026 1
< 0.1%
2799439100 1
< 0.1%
2320250281 1
< 0.1%
2264750694 1
< 0.1%
2071310218 1
< 0.1%
2052415039 1
< 0.1%
1922598800 1
< 0.1%
1671537444 1
< 0.1%
1663079059 1
< 0.1%
1520538536 1
< 0.1%

budget_in_usd
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct1839
Distinct (%)13.7%
Missing33174
Missing (%)71.1%
Infinite0
Infinite (%)0.0%
Mean19444573
Minimum1
Maximum2 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size728.8 KiB
2024-07-05T20:38:45.716809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile20000
Q11000000
median5500000
Q320000000
95-th percentile85000000
Maximum2 × 109
Range2 × 109
Interquartile range (IQR)19000000

Descriptive statistics

Standard deviation39113740
Coefficient of variation (CV)2.0115504
Kurtosis498.22308
Mean19444573
Median Absolute Deviation (MAD)5359000
Skewness12.248627
Sum2.6195729 × 1011
Variance1.5298846 × 1015
MonotonicityNot monotonic
2024-07-05T20:38:45.780236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5000000 361
 
0.8%
20000000 340
 
0.7%
10000000 336
 
0.7%
1000000 335
 
0.7%
15000000 301
 
0.6%
2000000 296
 
0.6%
25000000 267
 
0.6%
30000000 266
 
0.6%
3000000 265
 
0.6%
12000000 218
 
0.5%
Other values (1829) 10487
 
22.5%
(Missing) 33174
71.1%
ValueCountFrequency (%)
1 11
< 0.1%
1.13 1
 
< 0.1%
2 1
 
< 0.1%
2.26 1
 
< 0.1%
3.39 1
 
< 0.1%
4 3
 
< 0.1%
4.52 1
 
< 0.1%
5 4
 
< 0.1%
5.65 3
 
< 0.1%
7.91 1
 
< 0.1%
ValueCountFrequency (%)
2000000000 1
 
< 0.1%
414900000 1
 
< 0.1%
356000000 1
 
< 0.1%
350000000 2
 
< 0.1%
340000000 1
 
< 0.1%
321000000 1
 
< 0.1%
317000000 1
 
< 0.1%
300000000 5
< 0.1%
294700000 1
 
< 0.1%
291000000 1
 
< 0.1%

runtime_in_minutes
Real number (ℝ)

SKEWED  ZEROS 

Distinct118
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean71.450542
Minimum0
Maximum59460
Zeros11806
Zeros (%)25.3%
Negative0
Negative (%)0.0%
Memory size728.8 KiB
2024-07-05T20:38:45.839949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median87
Q398
95-th percentile114
Maximum59460
Range59460
Interquartile range (IQR)98

Descriptive statistics

Standard deviation417.61094
Coefficient of variation (CV)5.8447554
Kurtosis16106.945
Mean71.450542
Median Absolute Deviation (MAD)17
Skewness125.16983
Sum3332882
Variance174398.9
MonotonicityNot monotonic
2024-07-05T20:38:45.895787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 11806
25.3%
90 1991
 
4.3%
95 1101
 
2.4%
100 1088
 
2.3%
85 973
 
2.1%
93 970
 
2.1%
92 956
 
2.0%
91 921
 
2.0%
96 899
 
1.9%
98 886
 
1.9%
Other values (108) 25055
53.7%
ValueCountFrequency (%)
0 11806
25.3%
3 3
 
< 0.1%
4 1
 
< 0.1%
5 1
 
< 0.1%
6 3
 
< 0.1%
7 3
 
< 0.1%
9 1
 
< 0.1%
10 3
 
< 0.1%
11 2
 
< 0.1%
12 4
 
< 0.1%
ValueCountFrequency (%)
59460 1
 
< 0.1%
51420 1
 
< 0.1%
43200 1
 
< 0.1%
5220 1
 
< 0.1%
720 1
 
< 0.1%
420 1
 
< 0.1%
360 2
 
< 0.1%
300 3
 
< 0.1%
240 13
 
< 0.1%
180 33
0.1%

Interactions

2024-07-05T20:38:39.658638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-07-05T20:38:39.045523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-07-05T20:38:39.307829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-07-05T20:38:39.477264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-07-05T20:38:39.699960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-07-05T20:38:39.116010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-07-05T20:38:39.348045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-07-05T20:38:39.520694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-07-05T20:38:39.743672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-07-05T20:38:39.173072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-07-05T20:38:39.393338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-07-05T20:38:39.562859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-07-05T20:38:39.794161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-07-05T20:38:39.261738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-07-05T20:38:39.436221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-07-05T20:38:39.612378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-07-05T20:38:45.932249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
budget_in_usdratingrevenue_in_usdruntime_in_minutes
budget_in_usd1.0000.0980.7320.163
rating0.0981.0000.166-0.067
revenue_in_usd0.7320.1661.0000.067
runtime_in_minutes0.163-0.0670.0671.000

Missing values

2024-07-05T20:38:39.869066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-07-05T20:38:40.095396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-07-05T20:38:40.332971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

idtitleratingdirectorswritersstarsstorylineorigin_countrieslanguagesbudgetrevenueruntimegenresrevenue_in_usdbudget_in_usdruntime_in_minutes
0tt0060437Funeral in Berlin6.8​Guy Hamilton​Len Deighton, ​Evan Jones, ​Michael Caine, ​Oscar Homolka, ​Paul Hubschmid,Sent to East Berlin to retrieve a Communist defector, British spy Harry Palmer suspects the situation is not what his superiors believe it to be.United KingdomEnglish, GermanNaN$1831 hour 42 minutesThriller183.0NaN102
1tt0098300ShagNaN​Zelda Barron, ​Lanier Laney, ​Terry Sweeney, ​Robin Swicord,, ​Phoebe Cates, ​Bridget Fonda, ​Scott Coffey,Summer of 1963. Carson is getting married to her boyfriend so her friends Melaina, Pudge and Luanne take her to Myrtle Beach for one last irresponsible weekend.United States, United KingdomEnglish$5,000,000 (estimated)$6,957,9751 hour 38 minutesComedy, Drama, Romance6957975.05000000.098
2tt1853739You're Next6.6​Adam Wingard​Simon Barrett, ​Sharni Vinson, ​Joe Swanberg, ​AJ Bowen,When the Davison family comes under attack during their wedding anniversary getaway, the gang of mysterious killers soon learns that one of the victims harbors a secret talent for fighting back.United States, United KingdomEnglish$1,000,000 (estimated)$26,895,4811 hour 35 minutesDrama, Horror, Thriller26895481.01000000.095
3tt22036900Covid KarmaNaN​Biju ViswanathnoneStars, ​Rodney Norman, ​Christine Ohlman, ​Christopher Annino, noneStars​Biju ViswanathAn Indian film maker is stuck in USA for five months because of global pandemic. During this period, he repeatedly tries to make films, to maintain his sanity. The filmmaking process during Covid was a journey to hell; which teaches the filmmaker about his own disabilities and the screwed-up ways karma works.United StatesEnglishNaNNaN1 hour 1 minuteBiography, ComedyNaNNaN0
4tt0419434American HardcoreNaN​Paul RachmannoneWriter, ​Steven Blush, noneWriter, ​Greg Ginn, ​Ian MacKaye, ​James Drescher,The History of American Punk Rock 1980-1986United StatesEnglishNaN$376,0571 hour 40 minutesDocumentary, History, Music376057.0NaN100
5tt15073144Dark Ditties from Down UnderNaNnoneDirectors, ​Gerardo Chierchia, ​David Black, ​Fabio Segatori, noneDirectors​David Black, ​David Black, ​Fabricio Christian Amansi, ​Simay Argento,"Dark Ditties From Down Under" is a hosted anthology of the strange short films of David Black and Gerardo Chierchia.AustraliaEnglishNaNNaN1 hour 42 minutesAnimation, Action, AdventureNaNNaN102
6tt0082406The Fox and the Hound7.2noneDirectors, ​Ted Berman, ​Richard Rich, ​Art Stevens, noneDirectors, ​Daniel P. Mannix, ​Larry Clemmons, ​Ted Berman,, ​Mickey Rooney, ​Kurt Russell, ​Pearl Bailey,A fox named Tod and a hound named Copper vow to be best friends forever. But as Copper grows into a hunting dog, their unlikely friendship faces the ultimate test.United StatesEnglish$12,000,000 (estimated)$63,456,9881 hour 23 minutesAnimation, Adventure, Drama63456988.012000000.083
7tt0421729Big Momma's House 24.8​John Whitesell​Don Rhymer, ​Darryl Quarles, ​Martin Lawrence, ​Emily Procter, ​Nia Long,An FBI agent reprises his disguise as a corpulent old lady and takes a job as a nanny in a crime suspect's house.United StatesEnglish$40,000,000 (estimated)$141,522,9611 hour 39 minutesComedy, Crime141522961.040000000.099
8tt0077941Molière7.6​Ariane Mnouchkine​Ariane Mnouchkine, ​Philippe Caubère, ​Marie-Françoise Audollent, ​Frédéric Ladonne,Who was Moliere? He is known everywhere as one of the world's greatest playwrights. But who was he? Born Jean-Baptiste Poquelin in 1622, the son of a prosperous tapestry maker. His mother died when he was a boy. Growing up in the teeming streets of 17th century Paris, Jean Baptiste received a good Jesuit education and was fascinated by the street fairs and traveling c... Read allFrance, ItalyFrench, Latin, Greek, Ancient (to 1453), ItalianNaNNaN4 hours 20 minutesBiography, Drama, HistoryNaNNaN0
9tt10472748Uta no Prince Sama Maji Love Kingdom, The Movie6.7​Jouji Furuta, ​Tomoka NagaokanoneStars, ​Hiro Shimono, ​Jun'ichi Suwabe, ​Ken'ichi Suzumura, noneStars​Jouji Furuta, ​Tomoka NagaokaIdol groups Starish, Quartet Night and Heavens come together for a joint concert in this musical animated movie adaptation.JapanJapaneseNaN$11,597,8631 hour 24 minutesAnimation, Musical, Romance11597863.0NaN84
idtitleratingdirectorswritersstarsstorylineorigin_countrieslanguagesbudgetrevenueruntimegenresrevenue_in_usdbudget_in_usdruntime_in_minutes
46636tt7156922Rebel MagicNaNNaNNaNNaNNaNNaNNaNNaNNaNAnimation, Comedy, FantasyNaNNaN0
46637tt0338086HakerNaN​Janusz Zaorski​Jaroslaw Hess, ​Marek Nowowiejski, ​Janusz Zaorski, ​Bartosz Obuchowicz, ​Piotr Miazga, ​Kasia Smutniak,Two unlikely high-school friends share a common passion for computer hacking. Problems arise when their abilities are noticed by a group of gangsters.PolandEnglish, Polish, ItalianNaN$244,9221 hour 30 minutesAction, Comedy244922.0NaN90
46638tt13138488Dragons, Giants & WitchesNaN​Christopher Rawson​Jonathan Kydd​Christopher RawsonAnimated Stories about the various Myths, Legends and Tales about Firebreathing dragons, Mountain Tall Giants and Mysterious Magic-Wielding Witches.United KingdomEnglishNaNNaN1 hour 9 minutesAnimation, FantasyNaNNaN69
46639tt26246066The Silver TwilightNaN​Roy Thomas​Rick Ashley​Simon Broad, ​John Culkin, ​Pierre TremblayWhen the King of Monsters discovers that a group of rebel monsters are kidnapping innocent human beings, he sends his son into the human world with his magic staff and He is joined on the hunt by a group of young human rebels, and a bear.South KoreaEnglishHK$1,000 (estimated)NaN57 minutesAnimationNaN1000.057
46640tt0091083From Beyond6.6​Stuart Gordon, ​H.P. Lovecraft, ​Brian Yuzna, ​Dennis Paoli,, ​Jeffrey Combs, ​Barbara Crampton, ​Ted Sorel,A group of scientists have developed the Resonator, a machine which allows whoever is within range to see beyond normal perceptible reality. But when the experiment succeeds, they are immediately attacked by terrible life forms.United States, ItalyEnglish$4,500,000 (estimated)$1,261,0001 hour 25 minutesHorror, Sci-Fi1261000.04500000.085
46641tt0292352Wheatfield with CrowsNaN​Brent Roske​David Lawrence Baker, ​Brent Roske, ​Willie Wisely, ​David Carradine, ​Kate Clarke,'Wheatfield with Crows' takes the life of Vincent Van Gogh and sets it in the modern day music industry. With David Carradine, Theo Van Gogh, Kate Clarke and Henry Jaglom - featuring a soundtrack by Grammy nominated composer Willie Wisely, who also plays the lead character, Willie Vincent.NetherlandsNaNNaNNaNBiography, Drama, MusicNaNNaN0
46642tt1492959LiftedNaN​Lexi Alexander​Lexi Alexander, ​Nicki Aycox, ​Uriah Shelton, ​Dash Mihok,13 year old Henry Matthews struggles with life after his reservist father is deployed to Afghanistan. With the help of a local pastor, the boy decides to take part in a local singing contest.United StatesEnglishNaNNaN1 hour 48 minutesDrama, Music, WarNaNNaN108
46643tt27641085Sijjin5.3​Hadrah Daeng Ratu​Lele Laila, ​Ersan Özer, ​Ibrahim Risyad, ​Anggika Bolsterli, ​Messi Gusti,A young woman who uses black magic to threaten her cousin's wife.IndonesiaIndonesianNaNNaN1 hour 40 minutesHorror, ThrillerNaNNaN100
46644tt26525418Little Alice's Storytime: Through the Looking Glass Part 1NaN​Ken Thurlow​Ken Thurlow, ​Carol Bee, ​Paul Castro Jr., ​Simon Hill,Little Alice, Hatter, Dormouse and the Cheshire cat are here to share their fantastic adventures in Wonderland in Part 1 of this animated storytime.United StatesEnglishNaNNaN1 hour 5 minutesAnimationNaNNaN65
46645tt0030055Gorky 1: The Childhood of Maxim Gorky7.2​Mark Donskoy​Mark Donskoy, ​Maxim Gorky, ​Ilya Gruzdev, ​Aleksei Lyarsky, ​Varvara Massalitinova, ​Mikhail Troyanovskiy,A drama reveals the great writer's inauspicious early years as an orphan raised by conniving relatives.Soviet UnionRussianNaNNaN1 hour 38 minutesBiography, DramaNaNNaN98